Air flow management has been given utmost importance in data center thermal and energy management. A change in the air flow pattern may alter temperature of servers deployed in the data center. Not only the change the air flow pattern might be large in magnitude but the time-scale associated with such a change might also be very small. It has been observed that, the data center often employ variable frequency devices (VFDs) on computer room air conditioners (CRACs), in order to manipulate the air flow pattern of the CRAC according to heat load demand of the servers. The frequent change in the air flow pattern make it imperative for an online monitoring and control systems to have a mechanism that will respond to the changes in the same time frame. This necessitates fast temperature and CRAC influence prediction methods. The online monitoring and control systems also need to have knowledge of interactions between the servers and corresponding CRACs, so as to provide a solution to a particular hot-spot. Further, due to high demand on the data center even the design changes need to be very quick and yet thermally stable. To implement the design changes quickly and to ensure the thermal safety of the electronic equipment, there is a need for fast temperature and CRAC influence prediction models
The CFD technique has been used to model the data center and used as a base for developing the online monitoring and control systems. However, with above mentioned scenarios (such as design changes, CRAC location changes or CRACs with VFDs), the CFD method may found to be very time consuming and expensive. Also it has been observed that, many online control systems may not need the level of accuracy that a CFD model would provide. Only qualitative (instead of quantitative) knowledge of influence of CRACs on the servers/racks might also be sufficient. In such scenario, using CFD modeling turns out to be a very impractical option. This paves way for the use of reduced order models.
In order to reduce the number of CFD computations, a concept of reduced order modeling of the data center via proper orthogonal decomposition (POD) has been proposed by Samadiani and Joshi in a publication titled “Reduced order thermal modeling of data centers via proper orthogonal decomposition: a review.” published in International Journal of Numerical Methods for Heat & Fluid Flow. The publication involve collecting velocity or temperature observations from snapshot or reference CFD simulations and then processing the velocity or temperature in POD framework. The knowledge of the velocity profile at a location may not provide insights into system control. Also, known information about a new airflow configuration is generally in terms of mass flow rates and not in terms of the velocity. Therefore using the velocity as a POD variable may increase the computational complexity of the problem substantially. Further, the reduced order thermal modeling based on POD method needs a finite number of CFD simulations or experimental observations in order to form the basis of the computations required in the data center thermal and energy management. It may be understood that, the level of accuracy and the computations required largely depends upon a set of snapshots or reference cases. In view of the large number of possible how configurations involved in the data center with VFD installed CRAC; it is very tough to select the set of reference cases that will provide estimation of the temperature to the desired accuracy level. Therefore, it becomes a challenge to identify exact and appropriate set of the reference cases that may provide accurate prediction of the temperature for a given airflow configuration. Moreover, it is a challenge to quantify the exact offline CFD effort required for achieving a particular level of accuracy for any given airflow configuration of the CRAC.